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6 Python Data Validating Tools To Use In 2019

6 Python Data Validating Tools To Use In 2019

Ambika Choudhury

Python has become a dominant language in the field of data science and machine learning because of its various computational libraries supported by an extremely large community.

In this article, we list down 6 Python tools for data validation which can be useful for a data scientist. 

(The list is in no particular order)

1| Cerberus

While working on data, data validation is a crucial task which ensures that the data is cleaned, corrected and is useful. Cerberus is an open source data validation and transformation tool for Python. The library provides powerful and lightweight data validation functionality which can be easily extensible along with custom validation. The Cerberus 1.x versions can be used with Python 2 while version 2.0 and later rely on Python 3 features. 


Click here to install

2| Colander

Colander is a Python Library for validating and deserializing data which is obtained via XML, JSON, an HTML form post or any other equally simple data serialisation. It can be said as a good basis for form generation systems, data description systems, and configuration systems. The library has been tested on Python version 2.7 and above and can be used to define a data schema, serialise an arbitrary Python structure to a data structure composed of strings, mappings, and lists and deserialise a data structure composed of strings, mappings, and lists into an arbitrary Python structure after validating the data structure against a data schema.

Click here to install

3| Schema

Schema is a library for validating Python data structures such as those obtained from config-files, forms, external services or command-line parsing, converted from JSON/YAML (or something else) to Python data-types. If the data is valid, Schema.validate will return the validated data and if the data is invalid, Schema will raise SchemaError exception.

Click here to install

4| Voluptuous

Voluptuous is a Python data validation library. It is primarily intended for validating data coming into Python as JSON, YAML, etc. The library follows mainly three goals which are simplicity, support for complex data structures and providing useful error messages. There are several benefits of this library such as the validators are simple callables, errors are simple exceptions, schemas are basic Python data structures, etc.

See Also
AWS data exchange

Click here to install

5| Valideer

Valideer can be said as the lightweight data validation and adaptation library for Python. It supports both validations (check if a value is valid) and adaptation (convert a valid input to an appropriate output. It is extensible such as the new custom validators and adaptors can be easily defined and registered. The validation schemas can be specified in as declarative and extensible language. 

Click here to install.

6| Schematics

Schematics is a Python library for data validation which combines types into structures, validate them, and transform the shapes of your data based on simple descriptions. It can also be used in a range of tasks such as design and document specific data structures, convert structures to and from different formats such as JSON or MsgPack, validate API inputs, define message formats for communications protocols, like an RPC, and much more.

Click here to install.

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